RraR: Robust Recommendation Aggregation using Retraining in Internet of Things

Abstract

Trust and Reputation Systems (TRS) have been witnessing the most promising solutions to ensure the security of a system in the presence of insider adversaries. The methodology behind TRS is to analyze the behavior of entities in the system to compute trustworthiness on them by the mean of direct (using own experience) and indirect (using recommendations from the others) trust computation. For the Internet of Things (IoT), where devices collaborate with each other to offer multiple services, TRS solve the issues of access control, decision making, reliable service delivery etc. The focus of this paper is towards robust indirect trust computation in a service-oriented, dynamic IoT environment. We propose a robust recommendation aggregation scheme which alleviates the effect of false or dishonest recommendations in indirect trust computation by performing the objective and subjective evaluation. In the objective evaluation, the value of a received recommendation is weighted by computing deviation from the average value of all the recommendation received. Subjective evaluation is performed to weight the recommender based on their age of interaction. The scheme deploys a retraining module which retrains the credibility of a recommender based on the dissimilarity experienced with the outcome of recommendation. The effectiveness of the proposed scheme has been demonstrated by the results of the simulation.